The Pareto Principle says that 20 percent of the input (time, resources, effort) account for 80% percent of the output (results). For marketers, this means achieving optimal efficiency requires focusing 100 percent of your effort on only 20 percent of the leads and campaigns.
But to figure out which leads and campaigns to focus on, you need to analyze your customer data and predict which ones are most likely to convert. That’s predictive marketing — and it’s the biggest trend in demand generation today.
Behind the scenes of predictive marketing, big data from various sources is hard at work. Using artificial intelligence (AI) and machine-learning (ML) techniques, analytics tools can mine big data from various sources — your CRM tools, website downloads, event lists and unstructured data from every customer touchpoint — and learn a lot about your individual customers and prospects in a short amount of time. This analysis informs predictions about your customers: what they may be interested in buying, propensity to buy, and what stage of the buyer’s journey they’re in right now. This helps you decide whether to continue to nurture them through the funnel, refine your approach, and determine what content might be most relevant.
Such Intelligent Customer Engagement is the future of marketing, because only those companies that master it can attract and convert customers in today’s world of information overload.
The question is, what’s the state of your data? Is it ready for ICE?
Data-Quality Issues You Can Fix
The key to successful ICE is your data, but data quality issues are common. Here are three common data quality issues and how you can fix them to take full advantage of the power of predictive analytics for intelligent customer engagement.
- Your data is scattered and there’s no single source of truth. In a recent Forbes article, “single source of truth” is about managing disparate data across various systems and delivering the right data to decision makers.” Today, data is flying at us from all directions, and organizations struggle to collect and store it all. Data sources are often siloed in different tools owned by different teams. Dispersed across different systems and applications, it cannot be analyzed as a whole. Predictive analytics (and therefore ICE) can only be successful if all of your data is located in one place. Otherwise, AI and ML algorithms can only work with a portion of the data, and results will be inaccurate.
The fix: The first step is to identify all of your data sources. Know where the data is coming from and direct it all to a single system. That system could be your CRM, a data warehouse, a marketing automation system or a dedicated customer data platform. Only when all the data is in one place can you begin to ensure its accuracy and completeness, and make sure it’s healthy enough for predictive analysis to work.
- You haven’t defined data standards across your organization. Data standardization is the process of making sure your data is in a consistent format. This is a critical step in cleansing your data and eliminating any duplication. It helps to eliminate anomalies and outliers in your data and identify errors that can interfere with successful analysis. Because your data comes from various sources (structured and unstructured) standardization is essential.
The fix: Decide on the data values and preferences your organization will use so that records created from any source will normalize to the same format. Put processes in place for uploading lists, whether purchased or curated. Make sure your web form fields map to the fields in your source of truth and that data is validated upon entry to reduce the risk of bad data propagating to other systems. As you work to define your data standards, engage team members from Sales, Services, and other departments that rely on customer data for their success.
- Your systems aren’t talking to each other. So, you’ve determined Marketo is your source of truth, but there are disconnects. Say you update a field in Marketo, but that same field has a dependency in Salesforce that you forget to update. Or what if your fields in Salesforce aren’t properly mapped to Marketo? Setting up syncing incorrectly can also cause a disconnect. Keeping these complex systems maintained and properly integrated can be particularly challenging when you lack in-house resources or best-practice expertise and experience.
The Fix: Consider bringing in a third-party integration expert that can manage the process for you. Not only can they do the initial integration, but they can also stay engaged and perform CRM integration audits on an ongoing basis to ensure that everything created, deleted, or changed in one system maps properly to the other system. They can also help you implement and optimize data standards and processes to improve the efficiency of data transfers and eliminate inconsistencies between systems.
Data Insights Await
Data is the cornerstone of an effective Intelligent Customer Engagement strategy. Any level of predictive or personalized marketers depends on its quality and completeness. If you’re struggling to maintain consistent, high-quality customer data, don’t wait to engage a partner like DemandGen. Imagine what insights lay hidden in your data, and the opportunities you’ll create by engaging with your customers, armed with the intelligence those insights can provide.
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